MacBook Pro M4 Max 96GB经济之选
96 GB Unified (+48)
B
Adds memory headroom for longer context windows and future model growth.95.3 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Can DeepSeek R1 Distill 7B run on RTX A6000 48GB?
YES — Runs Great
B64Good
Estimated from fit model
DeepSeek R1 Distill 7B needs ~11.1 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~98 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 11.1 GB, 98.0 tok/s, Runs well
11.1 GB required48.0 GB available
23% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
33K
Memory
11.1 GB / 48.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom4.8 GB
See how fast it feels
See how fast it feelsDeepSeek R1 Distill 7B on RTX A6000 48GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]
The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
"""Sort a list using the quicksort algorithm.
Args:
arr: List of integers to sort.
Returns:
A new sorted list.
"""
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 98.0 tok/s | 1078 ms | 33K |
| Coding | B | Runs well | 98.0 tok/s | 1976 ms | 33K |
| Agentic Coding | B | Runs well | 98.0 tok/s | 2873 ms | 33K |
| Reasoning | B | Runs well | 98.0 tok/s | 2335 ms | 33K |
| RAG | B | Runs well | 98.0 tok/s | 3592 ms | 33K |
Quantization options
How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B58 |
Q3_K_S | 3 | 3.4 GB | Low | B58 |
NVFP4 | 4 | 3.9 GB | Medium | B58 |
Q4_K_M | 4 | 4.3 GB | Medium | B59 |
Q5_K_M | 5 | 5.0 GB | High | B59 |
Q6_K | 6 | 5.7 GB | High | B59 |
Q8_0 | 8 | 7.5 GB | Very High | B59 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B61 |
Get started
Copy-paste commands to run DeepSeek R1 Distill 7B on your machine.
Run
ollama run deepseek-r1:7b升级选项
能流畅运行 DeepSeek R1 Distill 7B 的硬件
Frequently asked questions
Can RTX A6000 48GB run DeepSeek R1 Distill 7B?
Yes, RTX A6000 48GB can run DeepSeek R1 Distill 7B with a B grade (Runs well). Expected decode speed: 98.0 tok/s.
How much VRAM does DeepSeek R1 Distill 7B need?
DeepSeek R1 Distill 7B (7B parameters) requires approximately 11.1 GB of memory with Q4_K_M quantization.
What is the best quantization for DeepSeek R1 Distill 7B?
The recommended quantization for DeepSeek R1 Distill 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will DeepSeek R1 Distill 7B run at on RTX A6000 48GB?
On RTX A6000 48GB, DeepSeek R1 Distill 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
Can RTX A6000 48GB run DeepSeek R1 Distill 7B for coding?
For coding workloads, DeepSeek R1 Distill 7B on RTX A6000 48GB receives a B grade with 98.0 tok/s and 33K context.
What context window can DeepSeek R1 Distill 7B use on RTX A6000 48GB?
On RTX A6000 48GB, DeepSeek R1 Distill 7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-qwen-7b-on-a6000-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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